OpenRaiser/Intern-Atlas huggingface dataset:
Trade-based Asset Model using Dynamic Junction Tree for Combinatorial Prediction Markets
Venue: arXiv cs.GT 2014
Year: 2014

Prediction markets have demonstrated their value for aggregating collective expertise. Combinatorial prediction markets allow forecasts not only on base events, but also on conditional and/or Boolean combinations of events. We describe a trade-based combinatorial prediction market asset management system, called Dynamic Asset Cluster (DAC), that improves both time and space efficiency over the method of, which maintains parallel junction trees for assets and probabilities. The basic data structure is the asset block, which compactly represents a set of trades made by a user. A user's asset model consists of a set of asset blocks representing the user's entire trade history. A junction tree is created dynamically from the asset blocks to compute a user's minimum and expected assets.
Interval Elicitation of Forecasts in a Prediction Market Reveals Lack of Anchoring "Bias"
Venue: arXiv cs.SI 2014
Year: 2014

In an online prediction market, forecasters who could not see the current state of the market until they made their own separate estimates moved their estimates closer to the market forecast when the current state of the market became known. Their first edits to the market forecast were very similar to the first edits of forecasters who could always see the current state of the market, and forecasters in both conditions had similar accuracy. These results suggest that our more elaborate forecast elicitation method might not improve forecasts and that any anchoring on the state of the market does not constitute an error in judgment.
Popularity and Performance: A Large-Scale Study
Venue: arXiv cs.SI 2014
Year: 2014

Social scientists have long sought to understand why certain people, items, or options become more popular than others. One seemingly intuitive theory is that inherent value drives popularity. An alternative theory claims that popularity is driven by the rich-get-richer effect of cumulative advantage---certain options become more popular, not because they are higher quality, but because they are already relatively popular. Realistically, it seems likely that popularity is driven by neither one of these forces alone but rather both together. Recently, researchers have begun using large-scale online experiments to study the effect of cumulative advantage in realistic scenarios, but there have been no large-scale studies of the combination of these two effects. We are interested in studying a case where decision-makers observe explicit signals of both the popularity and the quality of various options. We derive a model for change in popularity as a function of past popularity and past perceived quality. Our model implies that we should expect an interaction between these two forces---popularity should amplify the effect of quality, so that the more popular an option is, the faster we expect it to increase in popularity with better perceived quality. We use a data set from eToro.com, an online social investment platform, to support this hypothesis.
Interference Minimization in Asymmetric Sensor Networks
Venue: arXiv cs.CG 2014
Year: 2014

A fundamental problem in wireless sensor networks is to connect a given set of sensors while minimizing the \emph{receiver interference}. This is modeled as follows: each sensor node corresponds to a point in $\mathbb{R}^d$ and each \emph{transmission range} corresponds to a ball. The receiver interference of a sensor node is defined as the number of transmission ranges it lies in. Our goal is to choose transmission radii that minimize the maximum interference while maintaining a strongly connected asymmetric communication graph. For the two-dimensional case, we show that it is NP-complete to decide whether one can achieve a receiver interference of at most $5$. In the one-dimensional case, we prove that there are optimal solutions with nontrivial structural properties. These properties can be exploited to obtain an exact algorithm that runs in quasi-polynomial time. This generalizes a result by Tan et al. to the asymmetric case.
An optimization algorithm for multimodal functions inspired by collective animal behavior
Venue: arXiv cs.NE 2014
Year: 2014

Interest in multimodal function optimization is expanding rapidly since real world optimization problems often demand locating multiple optima within a search space. This article presents a new multimodal optimization algorithm named as the Collective Animal Behavior (CAB). Animal groups, such as schools of fish, flocks of birds, swarms of locusts and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic and to avoid predation. In the proposed algorithm, searcher agents are a group of animals which interact to each other based on the biological laws of collective motion. Experimental results demonstrate that the proposed algorithm is capable of finding global and local optima of benchmark multimodal optimization problems with a higher efficiency in comparison to other methods reported in the literature.
Hands-on experiments on intelligent behavior for mobile robots
Venue: arXiv cs.RO 2014
Year: 2014

In recent years, Artificial Intelligence techniques have emerged as useful tools for solving various engineering problems that were not possible or convenient to handle by traditional methods. AI has directly influenced many areas of computer science and becomes an important part of the engineering curriculum. However, determining the important topics for a single semester AI course is a nontrivial task, given the lack of a general methodology. AI concepts commonly overlap with many other disciplines involving a wide range of subjects, including applied approaches to more formal mathematical issues. This paper presents the use of a simple robotic platform to assist the learning of basic AI concepts. The study is guided through some simple experiments using autonomous mobile robots. The central algorithm is the Learning Automata. Using LA, each robot action is applied to an environment to be evaluated by means of a fitness value. The response of the environment is used by the automata to select its next action. This procedure holds until the goal task is reached. The proposal addresses the AI study by offering in LA a unifying context to draw together several of the topics of AI and motivating the students to learn by building some hands on laboratory exercises. The presented material has been successfully tested as AI teaching aide in the University of Guadalajara robotics group as it motivates students and increases enrolment and retention while educating better computer engineers.
Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)
Venue: arXiv cs.NE 2014
Year: 2014

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.
A Cost Minimization Approach to Synthesis of Linear Reversible Circuits
Venue: arXiv cs.ET 2014
Year: 2014

This paper presents a heuristic cost minimization approach to synthesizing linear reversible circuits. Two bidirectional linear reversible circuit synthesis methods are introduced, the Alternating Elimination with Cost Minimization method (AECM) and the Multiple CNOT Gate method (MCG). Algorithms, example syntheses, and extensions to these methods are presented. An MCG variant which incorporates line reordering is introduced. Tests comparing the new cost minimization methods with the best known method for large circuits are presented. Results show that of the three methods MCG had the lowest average CNOT gate counts for linear reversible circuits up to 24 lines, and that AECM had the lowest counts between 28 and 60 lines.
Velocity Selection for High-Speed UGVs in Rough Unknown Terrains using Force Prediction
Venue: arXiv cs.RO 2014
Year: 2014

Enabling high speed navigation of Unmanned Ground Vehicles (UGVs) in unknown rough terrain where limited or no information is available in advance requires the assessment of terrain in front of the UGV. Attempts have been made to predict the forces the terrain exerts on the UGV for the purpose of determining the maximum allowable velocity for a given terrain. However, current methods produce overly aggressive velocity profiles which could damage the UGV. This paper presents three novel safer methods of force prediction that produce effective velocity profiles. Two models, Instantaneous Elevation Change Model (IECM) and Sinusoidal Base Excitation Model: using Excitation Force (SBEM:EF), predict the forces exerted by the terrain on the vehicle at the ground contact point, while another method, Sinusoidal Base Excitation Model: using Transmitted Force (SBEM:TF), predicts the forces transmitted to the vehicle frame by the suspension.
Effects of Hard Real-Time Constraints in Implementing the Myopic Scheduling Algorithm
Venue: arXiv cs.OS 2014
Year: 2014

Myopic is a hard real-time process scheduling algorithm that selects a suitable process based on a heuristic function from a subset (Window)of all ready processes instead of choosing from all available processes, like original heuristic scheduling algorithm. Performance of the algorithm significantly depends on the chosen heuristic function that assigns weight to different parameters like deadline, earliest starting time, processing time etc. and the sizeof the Window since it considers only k processes from n processes (where, k<= n). This research evaluates the performance of the Myopic algorithm for different parameters to demonstrate the merits and constraints of the algorithm. A comparative performance of the impact of window size in implementing the Myopic algorithm is presented and discussed through a set of experiments.
Distinguishability Operations and Closures on Regular Languages
Venue: arXiv cs.FL 2014
Year: 2014

Given a regular language $L$, we study the language of words $\mathsf{D}(L)$, that distinguish between pairs of different left-quotients of $L$. We characterize this distinguishability operation, show that its iteration has always a fixed point, and we generalize this result to operations derived from closure operators and Boolean operators. We give an upper bound for the state complexity of the distinguishability operation, and prove its tightness. We show that the set of minimal words that can be used to distinguish between different left-quotients of a language $L$ has at most $n-1$ elements, where $n$ is the state complexity of $L$, and we also study the properties of its iteration. We generalize the results for the languages of words that distinguish between pairs of different right-quotients and two-sided quotients of a language $L$.
Automatic annotation of bioinformatics workflows with biomedical ontologies
Venue: arXiv cs.IR 2014
Year: 2014

Legacy scientific workflows, and the services within them, often present scarce and unstructured (i.e. textual) descriptions. This makes it difficult to find, share and reuse them, thus dramatically reducing their value to the community. This paper presents an approach to annotating workflows and their subcomponents with ontology terms, in an attempt to describe these artifacts in a structured way. Despite a dearth of even textual descriptions, we automatically annotated 530 myExperiment bioinformatics-related workflows, including more than 2600 workflow-associated services, with relevant ontological terms. Quantitative evaluation of the Information Content of these terms suggests that, in cases where annotation was possible at all, the annotation quality was comparable to manually curated bioinformatics resources.
Supervised learning in Spiking Neural Networks with Limited Precision: SNN/LP
Venue: arXiv cs.NE 2014
Year: 2014

A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural Networks. This novel algorithm uses limited precision for both synaptic weights and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for the supervised training. The results are comparable or better than previously published work. The results are applicable to the realization of large scale hardware neural networks. One of the trained networks is implemented in programmable hardware.
A Computational Model of Crowds for Collective Intelligence
Venue: arXiv cs.CY 2014
Year: 2014

In this work, we present a high-level computational model of IT-mediated crowds for collective intelligence. We introduce the Crowd Capital perspective as an organizational-level model of collective intelligence generation from IT-mediated crowds, and specify a computational system including agents, forms of IT, and organizational knowledge.
Inferring Social Structure and Dominance Relationships Between Rhesus macaques using RFID Tracking Data
Venue: arXiv cs.SI 2014
Year: 2014

In this paper we address the problem of inferring social structure and dominance relationships in a group of rhesus macaques (a species of monkey) using only position data captured using RFID tags. Automatic inference of the social structure in an animal group enables a number of important capabilities, including: 1) A verifiable measure of how the social structure is affected by an intervention such as a change in the environment, or the introduction of another animal, and 2) A potentially significant reduction in person hours normally used for assessing these changes. Social structure in a group is an important indicator of its members' relative level of access to resources and has interesting implications for an individual's health and learning in groups. There are two main quantitative criteria assessed in order to infer the social structure; Time spent close to conspecifics, and displacements. An interaction matrix is used to represent the total duration of events detected as grooming behavior between any two monkeys. This forms an undirected tie-strength (closeness of relationships) graph. A directed graph of hierarchy is constructed by using the well cited assumption of a linear hierarchy for rhesus macaques. Events that contribute to the adjacency matrix for this graph are withdrawals or displacements where a lower ranked monkey moves away from a higher ranked monkey. Displacements are one of the observable behaviors that can act as a strong indication of tie-strength and dominance. To quantify the directedness of interaction during these events we construct histograms of the dot products of motion orientation and relative position. This gives us a measure of how much time a monkey spends in moving towards or away from other group members.
Discovering New Sentiments from the Social Web
Venue: arXiv cs.SI 2014
Year: 2014

A persistent challenge in Complex Systems (CS) research is the phenomenological reconstruction of systems from raw data. In order to face the problem, the use of sound features to reason on the system from data processing is a key step. In the specific case of complex societal systems, sentiment analysis allows to mirror (part of) the affective dimension. However it is not reasonable to think that individual sentiment categorization can encompass the new affective phenomena in digital social networks. The present papers addresses the problem of isolating sentiment concepts which emerge in social networks. In an analogy to Artificial Intelligent Singularity, we propose the study and analysis of these new complex sentiment structures and how they are similar to or diverge from classic conceptual structures associated to sentiment lexicons. The conjecture is that it is highly probable that hypercomplex sentiment structures -not explained with human categorizations- emerge from high dynamic social information networks. Roughly speaking, new sentiment can emerge from the new global nervous systems as it occurs in humans.
Mathematical and Algorithmic Analysis of Network and Biological Data
Venue: arXiv cs.DS 2014
Year: 2014

This dissertation contributes to mathematical and algorithmic problems that arise in the analysis of network and biological data.
Polynomial Interpretations over the Natural, Rational and Real Numbers Revisited
Venue: arXiv cs.LO 2014
Year: 2014

Polynomial interpretations are a useful technique for proving termination of term rewrite systems. They come in various flavors: polynomial interpretations with real, rational and integer coefficients. As to their relationship with respect to termination proving power, Lucas managed to prove in 2006 that there are rewrite systems that can be shown polynomially terminating by polynomial interpretations with real (algebraic) coefficients, but cannot be shown polynomially terminating using polynomials with rational coefficients only. He also proved the corresponding statement regarding the use of rational coefficients versus integer coefficients. In this article we extend these results, thereby giving the full picture of the relationship between the aforementioned variants of polynomial interpretations. In particular, we show that polynomial interpretations with real or rational coefficients do not subsume polynomial interpretations with integer coefficients. Our results hold also for incremental termination proofs with polynomial interpretations.
Newton methods for k-order Markov Constrained Motion Problems
Venue: arXiv cs.RO 2014
Year: 2014

This is a documentation of a framework for robot motion optimization that aims to draw on classical constrained optimization methods. With one exception the underlying algorithms are classical ones: Gauss-Newton (with adaptive step size and damping), Augmented Lagrangian, log-barrier, etc. The exception is a novel any-time version of the Augmented Lagrangian. The contribution of this framework is to frame motion optimization problems in a way that makes the application of these methods efficient, especially by defining a very general class of robot motion problems while at the same time introducing abstractions that directly reflect the API of the source code.
Recent Advances in Joint Wireless Energy and Information Transfer
Venue: arXiv cs.NI 2014
Year: 2014

In this paper, we provide an overview of the recent advances in microwave-enabled wireless energy transfer (WET) technologies and their applications in wireless communications. Specifically, we divide our discussions into three parts. First, we introduce the state-of-the-art WET technologies and the signal processing techniques to maximize the energy transfer efficiency. Then, we discuss an interesting paradigm named simultaneous wireless information and power transfer (SWIPT), where energy and information are jointly transmitted using the same radio waveform. At last, we review the recent progress in wireless powered communication networks (WPCN), where wireless devices communicate using the power harvested by means of WET. Extensions and future directions are also discussed in each of these areas.
Understanding Repository Growth at the University of North Texas: A Case Study
Venue: arXiv cs.DL 2014
Year: 2014

Over the past decade the University of North Texas Libraries (UNTL) has developed a sizable digital library infrastructure for use in carrying out its core mission to the students, faculty, staff and associated communities of the university. This repository of content offers countless research possibilities for end users across the Internet when it is discovered and used in research, scholarship, entertainment, and lifelong learning. The characteristics of the repository itself provide insight into the workings of a modern digital library infrastructure, how it was created, how often it is updated, or how often it is modified. In that vein, the authors created a dataset comprised of information extracted from the UNT Libraries' archival repository Coda and analyzed this dataset in order to demonstrate the value and insights that can be gained from sharing repository characteristics more broadly. This case study presents the findings from an analysis of this dataset.
On the Predictability of Talk Attendance at Academic Conferences
Venue: arXiv cs.SI 2014
Year: 2014

This paper focuses on the prediction of real-world talk attendances at academic conferences with respect to different influence factors. We study the predictability of talk attendances using real-world tracked face-to-face contacts. Furthermore, we investigate and discuss the predictive power of user interests extracted from the users' previous publications. We apply Hybrid Rooted PageRank, a state-of-the-art unsupervised machine learning method that combines information from different sources. Using this method, we analyze and discuss the predictive power of contact and interest networks separately and in combination. We find that contact and similarity networks achieve comparable results, and that combinations of different networks can only to a limited extend help to improve the prediction quality. For our experiments, we analyze the predictability of talk attendance at the ACM Conference on Hypertext and Hypermedia 2011 collected using the conference management system Conferator.